Methods and Systems for Determining Health of an Organ Based on Medical Image Data Using Artificial Intelligence

4. The method of claim 3, wherein the labels indicate a) fully fatty degeneration, b) minimal residual soft-tissue density c) medium soft-tissue density and fat-density attenuation, d) predominant soft-tissue density attenuation, and e) non-fat attenuation and are respectively labeled by assigning values of 0, 1, 2, 3, and 7 respectively.

5. The method of claim 4, wherein the labels indicate a) fully fatty degeneration, b) minimal residual soft-tissue density c) medium soft-tissue density and fat-density attenuation, d) predominant soft-tissue density attenuation, and e) non-fat attenuation and are respectively labeled by assigning values of 0, 1, 2, 3, and 7 respectively.

6. The method of claim 5, wherein training the QC model comprises training the trained logistic regression model providing binary predictions with probabilities differentiating between normal thymus degradation and abnormalities by mapping the value of 0, 1, 2, and 3 to 0, and mapping the value of 7 to an affirmative binary value.

7. The method of claim 6, wherein training the score model comprises training the trained logistic regression model evaluating a level of fat and soft tissue in the thymus by mapping the value of 1, 2, and 3 to 1, and keeping the value of 0 to 0.

8. The method of claim 1, wherein applying the pre-processing comprises adjusting the scan intensities to fit within a specific range.

11. The method of claim 10, wherein the labels indicating health of organs comprises labels that indicate levels of fatty degeneration or non-fat attenuation.

12. The method of claim 10, wherein the set of labeled images comprise labels indicating the levels of fatty degeneration or non-fat attenuation in the thymus comprising labels that indicate a) fully fatty degeneration, b) minimal residual soft-tissue density c) medium soft-tissue density and fat-density attenuation, d) predominant soft-tissue density attenuation, and e) non-fat attenuation.

13. The method of claim 10, wherein the method further comprises providing a clinical solution, utilizing the one or more processors, and applying the clinical solution based on the health score.

15. The method of claim 14, wherein the first percentage is thirty-three percent and the second percentage is sixty-eight percent.

16. The method of claim 15, further comprising outputting the health score based on the generated classification output.